@inproceedings{10.1145/3412932.3412937,
author = {Pawlak, Wojciech Michal and Elsman, Martin and Oancea, Cosmin Eugen},
title = {A Functional Approach to Accelerating Monte Carlo Based American Option Pricing},
year = {2021},
isbn = {9781450375627},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3412932.3412937},
doi = {10.1145/3412932.3412937},
abstract = {We study the feasibility and performance efficiency of
expressing a complex financial numerical algorithm with high-level
functional parallel constructs. The algorithm we investigate is a
least-square regression-based Monte-Carlo simulation for pricing
American options. We propose an accelerated parallel implementation in
Futhark, a high-level functional data-parallel language. The Futhark
language targets GPUs as the compute platform and we achieve a
performance comparable to, and in particular cases up to 2.5X better
than, an implementation optimised by NVIDIA CUDA engineers. In
absolute terms, we can price a put option with 1 million simulation
paths and 100 time steps in 17 ms on a NVIDIA Tesla V100
GPU. Furthermore, the high-level functional specification is much more
accessible to the financial-domain experts than the low-level CUDA
code, thus promoting code maintainability and facilitating algorithmic
changes.},
booktitle = {Proceedings of the 31st Symposium on Implementation and Application of Functional Languages},
articleno = {5},
numpages = {12},
keywords = {high-performance computing, computational finance, parallel (GPU) programming, functional programming},
location = {Singapore, Singapore},
series = {IFL '19}
}